Recovery Rates and the Credit Crisis

What role did credit default swaps play in the credit crisis of 2007-09? New research by finance professor Jennifer Conrad indicates you might be wrong to point fingers at the derivatives market.

Conrad has proposed a new way to estimate default probabilities. By comparing default probabilities in the credit default swap (CDS) market and equity option market, Conrad and her colleagues generated a new estimate of how asset values in firms changed through the credit crisis.

To examine if credit default swap contracts drive changes in the equity market, Conrad collaborated with Robert F. Dittmar of the University of Michigan and Allaudeen Hameed of the National University of Singapore. They report their findings in “Cross-Market and Cross-Firm
Effects in Implied Default Probabilities and Recovery Values.”

“With these estimates, we can measure the extent to which problems in financial firms had an impact on nonfinancial firms in this period – how Wall Street affected Main Street,” Conrad said.

A credit default swap is an insurance contract on a bond. If the company issuing the bond starts to struggle, the value of the insurance contract, or CDS, increases – reflecting a higher probability of default. If many people buy such insurance contracts, the price of the insurance
might also increase, and market participants might infer that the firm is struggling – causing price changes in the underlying market for the firm’s securities.

In the early days of the credit crisis, worries arose that speculators in the CDS market were causing such price swings. Conrad uses the analogy of an investor buying auto insurance on someone else’s car, putting the investor in the position of hoping the driver has an accident
so that the investor can make money. “People felt that some investors were doing that with the CDS market,” she said. “They were buying the insurance contract and hoping for – or, perhaps through their trading, contributing to – the company’s troubles.”

To measure links between CDS and equity markets, the researchers looked at each market’s best estimate of the likelihood of a firm defaulting. They estimated default probabilities for 35 financial and 123 nonfinancial firms before and during the credit crisis. They found that the two markets’ estimates of the chances of default tracked fairly closely, even more closely once the crisis began. But before the credit crisis, the CDS market appeared more optimistic than the equity options market, both about the chances that a firm might default and the estimate of its assets’ worth if it did. By looking at default probabilities across markets, the researchers estimated the market participants’ view of asset values over this period.

“One thing that surprised me was that in many cases people assume asset values in the event of default, or recovery rates, are the same across firms and across time,” she said. Returning to the car insurance analogy, Conrad explains that if the risk – the probability of a crash – is the same for two drivers, their insurance prices should depend on the damage estimate in an accident. A car driven at 65 mph will suffer extensive damage and have a low recovery rate, compared to a car going 15 mph, where there will be less damage and a higher recovery rate. Someone assuming that the two drivers would suffer the same damage might look at the difference in their insurance prices and infer (incorrectly) that the probability of their being in an accident was quite different. The researchers use an independent estimate of default from the equity option market that allowed them to back out the CDS’ market estimate of recovery rates and see if the rates differed across firms and changed across time.

Conrad found very strong evidence that recovery rates were different across both time and firms. “The direction of the effect wasn’t surprising,” she said, “the magnitude was.” Recovery rates declined precipitously early in the crisis, edged up in late 2008, and dropped in spring 2009. Recovery rates also differed significantly between financial and nonfinancial firms, with a much steeper decline in rates for financial firms.

Once the researchers had estimates of default probability and recovery rates, they could examine if elevated default probabilities in one firm caused changes in the default probabilities or recovery rates of other firms. For example, if one bank goes under and sells its assets under
fire-sale conditions, does that affect the value of similar assets of another bank?

“If there was transmission of bad news from one bank to another,” she said, “we might be able to measure it.”

Similarly, these estimates can measure cross-effects between companies in the financial sector and nonfinancial industries. Conrad showed that changes in the probability of survival of one financial firm affect other firms in that sector. And an increase in the implied default probability of financial firms leads to a decrease in the asset values of other financial firms, consistent with fire-sale effects.

Conrad and her co-authors are looking at smaller subsets of firms within the financial industry to identify how risks are propagated within the sector and if some types of financial firms have larger effects on other nonfinancial firms.

“If I measure effects across all of the big financial players,” she said, “will I find one firm, or a type of firm, that’s particularly important in how its changes in probability of survival affect the other firms in that sector?”

Key take-aways

The CDS market does not appear to unduly drive changes in estimates of the default probability implied in the equity option market.

During the 2007-09 credit crisis, financial firms suffered both more dramatic decreases in their probability of survival and declines in their asset values in the event of default than did nonfinancial firms.

What happens on Wall Street affects Main Street, but evidence of the effect going the other way is much weaker.